Mangroves and coastal topography create economic “safe havens” from tropical storms

Data construction

We construct an annual panel dataset from 2000 to 2012 of 2549 coastal communities within 102 countries. Population counts from 2000 to 2012 for each community were calculated from the Landscan population database²⁷ and coastal communities were defined as the lowest level administration units with an ocean coastline of each country using the Global Administrative Areas Database v2.7. Using the National Oceanic and Atmospheric Administration’s (NOAA) global nighttime lights data, we examine trends in economic activity before and after a cyclone event. The growth rate in average annual luminosity from nighttime lights trends with economic growth and has been used as an effective proxy for local economic activity^{22,24,28,29,30,31,32}.

However, trends in nighttime luminosity should not be interpreted as a measure of economic growth. Instead, we focus on tracking the dynamic impacts of nighttime luminosity (e.g. deviations from trends) that indicates whether an exposed community’s economic activity recovers or suffers permanent damage. The average elevation of each coastal community was calculated using a void-filled Shuttle Radar Topography Mission (SRTM) data at 3 arc-seconds, or approximately 90 m² at the equator³³. The SRTM has the potential to result in an overestimation of elevation in heavily built environment areas or areas of dense high forest canopies compared against locations without such trees. However, during the timeframe of our analysis, the SRTM product was the most appropriate and available product.

The mangrove coverage dataset was adapted from the Continuous Global Mangrove Forest Cover for the 21st Century (CGMFC-21) database for the years 2000 to 2012¹². The coastline length of each community, based on Global Self-Consistent, Hierarchical, High-Resolution Shoreline Database v2.3.5 full resolution data³⁴, was used to normalize the area of mangroves offshore of each coastal community creating a measurement for the “width” of mangroves per meter of coastline.

Tropical storm locations for all years were recreated from the International Best Track Archive for Climate Stewardship (IBTrACS) Annual Tropical Cyclone Best Track Database³⁵. Precise measurements of exposure, combined with high-resolution luminosity data, allows to distinguish the heterogeneous impacts of cyclones on exposed communities and the capacity for mangroves to shelter coastal economic activity at different elevations and for different mangrove widths. The intensity of exposure is measured by the distance of the cyclone’s “eye” from the exposed village’s nearest boundary.

Tropical cyclone wind profile³⁶, villages passing within 100 km of the cyclone’s eye were likely to experience maximum wind velocity and surface level pressure whereas those villages passing within more distant bands—i.e., 100–200 km and 200–300 km, were likely to experience similar surface level pressure but a non-linear reduction in wind velocity. Binning wind velocities in this way recognizes the highly non-linear relationship between wind velocity and on-the-ground damages from cyclone events³⁷. We therefore expect the capacity for mangroves and elevation to shelter economic activity also to depend on this intensity of exposure.

Our full sample encompasses nearly 400 million individuals in 102 countries and 2549 mangrove-holding communities (Table 1). Based on 2019 fiscal year World Bank categorizations, most of our sample resides in developing countries (85.1%) with 46.7% in lower-middle income (gross national income/per capita between $996 and $3895) and 35.3% in upper-middle income countries (gross national income/ per capita between $3896 and $12,056). We also find that most mangrove coverage in our sample exists within developing countries (88.7%) and overwhelmingly in upper-middle income countries (56.0%) in the Latin America and Caribbean (LAC) and East Asian and Pacific (EAP) developing regions. While only 14.9% of our sample’s global population resides in LAC countries, these countries account for 39.8% of mangrove holdings in our sample whereas the 45.5% of the population residing in EAP countries only account for 30.3% of mangrove coverage.

Empirical strategy

We use a distributed-lag autoregressive model to measure the initial and permanent effect of cyclone exposure on economic activity in coastal communities. The growth in economic activity for each coastal community is proxied by the difference in logs between years, (growth={ln}left(luminosit{y}_{t}right)-{ln}left(luminosit{y}_{t-1}right)). Our estimating equation is

where the (beta) coefficients capture the marginal effects, across three bins of cyclone exposure, on the growth rate of luminosity for the (j{^{prime}}th) administrative unit, within country (i), and in time (t-L) where (t) is the observed year and L is the number of lags ranging from (0 ; to ;n). Here, ({C}_{i,j,t}) is a vector of cyclone exposures binned by the distance from the cyclone’s “eye” to the nearest boundary of the exposed community (< 100 km, 100–200 km and 200–300 km). The distributed-lag autoregressive model predicts (growt{h}_{i,j,t}) using a vector of cyclone exposures in previous, current and future years—i.e., forward and backward lags. Such an approach isolates the marginal impacts of a cyclone exposure on growth trends without being confounded by multiple reoccurring exposures.

We adopt community-specific and year-specific fixed effects to control for any unobservable impacts, captured by (gamma) and (delta), on economic activity for a given community or within a given year. Mangrove width and the logged baseline level of luminosity (digital number units—i.e., DN) are added as control variables in the vector, ({x}_{i,j,t},) as well as a linear trend to absorb background growth trends that are shared by communities in our sample. Four autoregressive lags are also included in all specifications and robust standard errors are reported.

The impact of a cyclone on long-run trends in economic activity (z) years later is

which is cumulative effect (summation of marginal effects) of cyclone exposure on luminosity growth. To examine the scope for mangroves of varying width and topography of varying elevation to shelter coastal economic activity, we stratify our sample into four subsamples: “low and narrow”, “low and wide”, “high and narrow” and “high and wide”. “Low” sub-samples contain coastal communities with a mean elevation < 50 m and “narrow” sub-samples contain coastal communities with a mangrove width of < 10 m per meter of coastline (Fig. 2).

The 50 m elevation and 10 m mangrove distance thresholds were chosen to provide some balance in statistical power to our four subsamples. Here, the unit of observation in our analysis is the lowest available administrative unit. A location with an average coastal elevation of 50 m elevation is likely to have a large and vulnerable population living in low-lying areas. Similarly, a coastline with an average of 10 m of mangroves per m of coastline is likely to have vast expanses of mangroves in some of its locations. However, the average area of our unit of observation is 887 square kilometers and the average administrative unit’s coastline length is 32 km. As such, those low-lying populations and expansive mangroves get absorbed into the aggregation process rather quickly. These thresholds are only meant to facilitate the binning of our sample into four groups (binary low vs. high elevation, binary narrow vs. wide mangroves).

We hypothesize that communities in subsample 1, lacking natural protections against storm exposure, are the most vulnerable and would experience the strongest effect on long-run economic outcomes. We further hypothesize that communities in subsample 4, receiving protection from mangroves and topography, would be the most insulated against storm exposures. Likewise, we would expect communities benefiting from either expansive mangroves or high elevation, would be partially protected from exposure and adverse long-run economic impacts. In terms of “intensity of exposure”, we hypothesize that those communities passing within the nearest proximity to the cyclone’s “eye” will experience the largest adverse impact on economic growth—i.e., bin 1 > bin 2 > bin 3.

Source: Ecology - nature.com