Data
# === SAMPLE FILTERS (adjust here) ===
MIN_CAPACITY_MCM <- 1 # Set to NA to skip capacity filter
REQUIRE_VOLUME <- TRUE # Set to TRUE to drop dams without volume_max
analysis_data <- readRDS(file.path(strategic_dir,
"strategic_placement_analysis_data.rds"))
# Apply sample filters
n_full <- nrow(analysis_data)
if (REQUIRE_VOLUME) {
analysis_data <- analysis_data %>% filter(!is.na(capacity_mcm))
}
if (!is.na(MIN_CAPACITY_MCM)) {
analysis_data <- analysis_data %>%
filter(is.na(capacity_mcm) | capacity_mcm >= MIN_CAPACITY_MCM)
}
# Check which version columns are available
has_v1 <- "suitability_ratio_v1" %in% names(analysis_data)
has_v3 <- "suitability_ratio_v3" %in% names(analysis_data)
# Check if constrained (same-country) columns are available
has_constrained <- "suitability_ratio_c" %in% names(analysis_data) &&
sum(!is.na(analysis_data$suitability_ratio_c)) > 30
has_constrained_v1 <- has_constrained &&
"suitability_ratio_c_v1" %in% names(analysis_data)
has_constrained_v3 <- has_constrained &&
"suitability_ratio_c_v3" %in% names(analysis_data)
# Fix: Force SR_c = SR_u for domestic dams (centroid-based country assignment
# can misclassify border-adjacent segments, creating spurious SR_u != SR_c)
if (has_constrained) {
dom <- analysis_data$transboundary == 0
analysis_data$suitability_ratio_c[dom] <- analysis_data$suitability_ratio[dom]
analysis_data$optimal_outside_country[dom] <- FALSE
for (v in c("v1", "v2", "v3")) {
col_c <- paste0("suitability_ratio_c_", v)
col_u <- paste0("suitability_ratio_", v)
col_out <- paste0("optimal_outside_", v)
if (col_c %in% names(analysis_data))
analysis_data[[col_c]][dom] <- analysis_data[[col_u]][dom]
if (col_out %in% names(analysis_data))
analysis_data[[col_out]][dom] <- FALSE
}
}
# Transboundary subset with along-river border distance
tb_data <- analysis_data %>%
filter(transboundary == 1, !is.na(dam_dist_border))
1. Sample Overview
This section describes the analysis sample after filtering from the
Global Dam Tracker (GDAT) universe. Dams are retained if they can be
matched to a HydroRIVERS segment, have a recorded completion year
between 1950–2025, and have a known primary purpose. The suitability
computation considers all candidate river segments within a $$200 km
buffer along the river network, scoring each on hydropower potential
(flow, catchment area, hydraulic head) and population exposure at the
time of construction.
tibble(
Statistic = c("Total dams", "Transboundary", "Domestic",
"TB with along-river border distance",
"Mean candidates per dam",
"Median candidates per dam"),
Value = c(
nrow(analysis_data),
sum(analysis_data$transboundary),
sum(1 - analysis_data$transboundary),
nrow(tb_data),
sprintf("%.0f", mean(analysis_data$n_candidates, na.rm = TRUE)),
sprintf("%.0f", median(analysis_data$n_candidates, na.rm = TRUE))
)
) %>%
kbl(caption = "Sample Summary") %>%
kable_styling(bootstrap_options = c("striped", "hover"),
full_width = FALSE)
Sample Summary
|
Statistic
|
Value
|
|
Total dams
|
4901
|
|
Transboundary
|
2634
|
|
Domestic
|
2267
|
|
TB with along-river border distance
|
745
|
|
Mean candidates per dam
|
156
|
|
Median candidates per dam
|
59
|
analysis_data %>%
count(purpose_category, transboundary) %>%
mutate(transboundary = ifelse(transboundary == 1,
"Transboundary", "Domestic")) %>%
pivot_wider(names_from = transboundary, values_from = n, values_fill = 0) %>%
mutate(Total = Domestic + Transboundary) %>%
arrange(desc(Total)) %>%
kbl(caption = "Dams by Purpose and Transboundary Status") %>%
kable_styling(bootstrap_options = c("striped", "hover"),
full_width = FALSE)
Dams by Purpose and Transboundary Status
|
purpose_category
|
Domestic
|
Transboundary
|
Total
|
|
Other
|
692
|
688
|
1380
|
|
Flood Control
|
489
|
656
|
1145
|
|
Irrigation
|
456
|
598
|
1054
|
|
Hydropower
|
336
|
396
|
732
|
|
Water Supply
|
294
|
296
|
590
|
analysis_data %>%
count(decade, transboundary) %>%
mutate(transboundary = ifelse(transboundary == 1,
"Transboundary", "Domestic")) %>%
pivot_wider(names_from = transboundary, values_from = n, values_fill = 0) %>%
mutate(Total = Domestic + Transboundary) %>%
kbl(caption = "Dams by Decade") %>%
kable_styling(bootstrap_options = c("striped", "hover"),
full_width = FALSE)
Dams by Decade
|
decade
|
Domestic
|
Transboundary
|
Total
|
|
1950
|
363
|
402
|
765
|
|
1960
|
671
|
705
|
1376
|
|
1970
|
538
|
651
|
1189
|
|
1980
|
383
|
436
|
819
|
|
1990
|
220
|
220
|
440
|
|
2000
|
67
|
131
|
198
|
|
2010
|
24
|
63
|
87
|
|
2020
|
1
|
26
|
27
|
2. Suitability Ratios Across Specifications
We compute three alternative suitability indices to ensure our
results are not driven by a particular functional form:
- V1 (Baseline): Raw product of discharge, upstream
catchment area, and hydraulic head. Heavily right-skewed because a few
candidate sites with large rivers dominate the score.
- V2 (Gradient-weighted): Applies square-root
transforms to flow and catchment area, giving less weight to extreme
values. This is our primary specification.
- V3 (Percentile Ranks): Converts each component to
its within-river-system percentile rank before combining. This ensures
each component contributes roughly equally and yields ratios on a more
interpretable 0–1 scale.
The suitability ratio divides a dam’s actual site score by the best
available score within $$200 km along the river network. A ratio of 1
means the dam sits at the optimal site; lower values indicate the dam
was placed at a less suitable location relative to nearby
alternatives.
versions <- c("v1", "v2", "v3")[c(has_v1, TRUE, has_v3)]
suit_summary <- analysis_data %>%
mutate(tb_label = ifelse(transboundary == 1,
"Transboundary", "Domestic")) %>%
group_by(tb_label) %>%
summarise(
n = n(),
across(
all_of(paste0("suitability_ratio_", versions)),
list(mean = ~mean(.x, na.rm = TRUE),
median = ~median(.x, na.rm = TRUE)),
.names = "{.col}_{.fn}"
),
.groups = "drop"
)
suit_summary %>%
kbl(digits = 3,
caption = "Suitability Ratio by Specification and TB Status") %>%
kable_styling(bootstrap_options = c("striped", "hover"),
full_width = FALSE) %>%
add_header_above(c(" " = 2,
"V1 (Baseline)" = 2,
"V2 (Gradient)" = 2,
"V3 (Percentile)" = 2))
Suitability Ratio by Specification and TB Status
|
|
V1 (Baseline)
|
V2 (Gradient)
|
V3 (Percentile)
|
|
tb_label
|
n
|
suitability_ratio_v1_mean
|
suitability_ratio_v1_median
|
suitability_ratio_v2_mean
|
suitability_ratio_v2_median
|
suitability_ratio_v3_mean
|
suitability_ratio_v3_median
|
|
Domestic
|
2267
|
0.146
|
0.009
|
0.241
|
0.111
|
0.696
|
0.706
|
|
Transboundary
|
2634
|
0.080
|
0.002
|
0.140
|
0.057
|
0.647
|
0.655
|
Quick t-tests (no controls)
trb <- analysis_data %>% filter(transboundary == 1)
dom <- analysis_data %>% filter(transboundary == 0)
for (v in versions) {
col <- paste0("suitability_ratio_", v)
tt <- t.test(trb[[col]], dom[[col]])
cat(sprintf(
"**%s**: TRB mean = %.3f, DOM mean = %.3f, diff = %.3f (p = %.4f) \n",
toupper(v), tt$estimate[1], tt$estimate[2],
tt$estimate[1] - tt$estimate[2], tt$p.value
))
}
V1: TRB mean = 0.080, DOM mean = 0.146, diff =
-0.066 (p = 0.0000)
V2: TRB mean = 0.140, DOM mean = 0.241, diff = -0.101
(p = 0.0000)
V3: TRB mean = 0.647, DOM mean = 0.696, diff = -0.049
(p = 0.0000)
Across all three specifications, transboundary dams have lower mean
suitability ratios than domestic dams, and the differences are
statistically significant at conventional levels. The gap is largest for
V1 and V2, which are more sensitive to extreme suitability scores on
large rivers. V3 compresses the distribution by construction (percentile
ranks are bounded between 0 and 1), so the raw gap is smaller, but it
remains highly significant. This pattern is consistent with the
hypothesis that transboundary dams face political or strategic
constraints that lead to placement at sites that are sub-optimal from a
purely engineering perspective.
3. Suitability Ratio Density Plots
The density plots below visualize the full distribution of
suitability ratios for domestic and transboundary dams. Key patterns to
look for: (1) whether the transboundary distribution is shifted leftward
(toward worse sites), and (2) whether the shapes differ—for instance, a
heavier left tail for transboundary dams would suggest a subset of
particularly poorly-sited dams.
plot_density <- function(data, col, vlabel) {
ggplot(data, aes(x = .data[[col]],
fill = factor(transboundary))) +
geom_density(alpha = 0.6) +
geom_vline(xintercept = 1, linetype = "dashed", color = "black") +
scale_fill_manual(
values = c("0" = "steelblue", "1" = "coral"),
labels = c("Domestic", "Transboundary"),
name = ""
) +
labs(
title = paste("Distribution of Suitability Ratios -", vlabel),
subtitle = "Ratio of 1 = dam at optimal location; lower = worse site quality",
x = "Suitability Ratio (Actual / Optimal)",
y = "Density"
) +
theme_minimal(base_size = 13) +
theme(legend.position = "bottom")
}
V3 (Percentile Ranks)
plot_density(analysis_data, "suitability_ratio_v3",
"V3 (Percentile Ranks)")
V2 (Gradient-weighted)
plot_density(analysis_data, "suitability_ratio_v2",
"V2 (Gradient-weighted)")
V1 (Baseline)
plot_density(analysis_data, "suitability_ratio_v1",
"V1 (Baseline)")
4. Suitability by Dam Purpose
Different dam purposes may have different site selection criteria.
Hydropower dams, for example, are strongly constrained by topography and
river flow, so siting distortions may be more visible. Irrigation dams
may have more flexibility in placement. The bar charts below show mean
suitability ratios by purpose category, separately for domestic and
transboundary dams, with 95% confidence intervals.
plot_purpose <- function(data, col, vlabel) {
pdata <- data %>%
group_by(transboundary, purpose_category) %>%
summarise(
mean_suit = mean(.data[[col]], na.rm = TRUE),
se_suit = sd(.data[[col]], na.rm = TRUE) / sqrt(n()),
n = n(),
.groups = "drop"
) %>%
filter(n >= 5) %>%
mutate(tb_label = ifelse(transboundary == 1,
"Transboundary", "Domestic"))
ggplot(pdata, aes(x = purpose_category, y = mean_suit,
fill = tb_label)) +
geom_col(position = position_dodge(width = 0.8), width = 0.7) +
geom_errorbar(
aes(ymin = mean_suit - 1.96 * se_suit,
ymax = mean_suit + 1.96 * se_suit),
position = position_dodge(width = 0.8), width = 0.3
) +
scale_fill_manual(values = c("Domestic" = "steelblue",
"Transboundary" = "coral")) +
labs(
title = paste("Suitability by Purpose -", vlabel),
subtitle = "Error bars show 95% confidence intervals",
x = "Dam Purpose", y = "Mean Suitability Ratio", fill = ""
) +
theme_minimal(base_size = 13) +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1))
}
V3 (Percentile Ranks)
plot_purpose(analysis_data, "suitability_ratio_v3",
"V3 (Percentile Ranks)")
V2 (Gradient-weighted)
plot_purpose(analysis_data, "suitability_ratio_v2",
"V2 (Gradient-weighted)")
V1 (Baseline)
plot_purpose(analysis_data, "suitability_ratio_v1",
"V1 (Baseline)")
5. Position Deviation from Optimal
Position deviation measures the distance (in km along the river)
between where a dam was actually built and where the
suitability-maximizing site would have been. Positive values mean the
dam was placed upstream of the optimal site; negative values mean
downstream. If transboundary dams are systematically displaced from
optimal locations—particularly toward upstream positions where the river
is still within the dam-building country’s territory—this would suggest
strategic positioning motivations.
plot_posdev <- function(data, col, vlabel) {
ggplot(data, aes(x = .data[[col]],
fill = factor(transboundary))) +
geom_density(alpha = 0.6) +
geom_vline(xintercept = 0, linetype = "dashed", color = "black") +
scale_fill_manual(
values = c("0" = "steelblue", "1" = "coral"),
labels = c("Domestic", "Transboundary"),
name = ""
) +
coord_cartesian(xlim = c(-200, 200)) +
labs(
title = paste("Position Deviation from Optimal -", vlabel),
subtitle = "Positive = upstream of optimal; negative = downstream",
x = "Position Deviation (km)", y = "Density"
) +
theme_minimal(base_size = 13) +
theme(legend.position = "bottom")
}
V3 (Percentile Ranks)
plot_posdev(analysis_data, "position_deviation_km_v3",
"V3 (Percentile Ranks)")
V2 (Gradient-weighted)
plot_posdev(analysis_data, "position_deviation_km_v2",
"V2 (Gradient-weighted)")
V1 (Baseline)
plot_posdev(analysis_data, "position_deviation_km_v1",
"V1 (Baseline)")
6. Transboundary Dams: Suitability vs Border
Distance
For the subset of transboundary dams where we can measure along-river
distance to the nearest international border crossing, we examine
whether proximity to the border is associated with worse site quality.
If the transboundary suitability penalty is driven by geographic
constraints near borders (e.g., narrow valleys, unfavorable terrain at
boundary locations), we would expect dams closer to the border to have
systematically lower suitability. If instead the penalty reflects
broader political-strategic considerations, the relationship between
border distance and suitability may be weak or absent.
plot_border <- function(data, col, vlabel) {
ggplot(data, aes(x = dam_dist_border, y = .data[[col]])) +
geom_point(alpha = 0.5, color = "coral") +
geom_smooth(method = "lm", se = TRUE, color = "darkred") +
labs(
title = paste("TB Dams: Suitability vs Border Distance -", vlabel),
subtitle = "Along-river distance to nearest border crossing",
x = "Distance to Border (km, along river)",
y = "Suitability Ratio"
) +
theme_minimal(base_size = 13)
}
V3 (Percentile Ranks)
plot_border(tb_data, "suitability_ratio_v3", "V3 (Percentile Ranks)")
V2 (Gradient-weighted)
plot_border(tb_data, "suitability_ratio_v2", "V2 (Gradient-weighted)")
V1 (Baseline)
plot_border(tb_data, "suitability_ratio_v1", "V1 (Baseline)")
7. Sensitivity: Transboundary Coefficient Across Specifications
A key concern is whether the transboundary suitability gap is an
artifact of our suitability index construction. To address this, we run
the same regression model (continent and decade fixed effects, standard
errors clustered by continent) using each of the three suitability
specifications as the dependent variable. If the transboundary
coefficient is consistently negative across specifications with very
different functional forms, this increases confidence that the result
reflects a real phenomenon rather than a measurement artifact.
Note that the V3 (percentile) coefficient is expected to be smaller
in magnitude since percentile ranks mechanically compress the scale. The
relevant comparison is the sign, significance, and consistency of the
coefficient across specifications.
analysis_data <- analysis_data %>%
mutate(decade = floor(as.numeric(year_fin) / 10) * 10)
sens_models <- list()
sens_coefs <- list()
for (v in versions) {
col <- paste0("suitability_ratio_", v)
fml <- as.formula(paste(col,
"~ transboundary + log_capacity + hydropower + irrigation",
"| continent + decade"))
m <- feols(fml, data = analysis_data, cluster = ~continent)
sens_models[[v]] <- m
sens_coefs[[v]] <- tibble(
spec = v,
coef = coef(m)["transboundary"],
se = se(m)["transboundary"],
pval = pvalue(m)["transboundary"]
)
}
sens_df <- bind_rows(sens_coefs) %>%
mutate(
spec_label = case_when(
spec == "v1" ~ "V1 (Baseline)",
spec == "v2" ~ "V2 (Gradient)",
spec == "v3" ~ "V3 (Percentile)"
),
sig = case_when(
pval < 0.01 ~ "***",
pval < 0.05 ~ "**",
pval < 0.10 ~ "*",
TRUE ~ ""
)
)
sens_df %>%
select(Specification = spec_label,
Coefficient = coef, SE = se,
`p-value` = pval, Sig = sig) %>%
kbl(digits = 4,
caption = "Transboundary Coefficient Across Suitability Specifications") %>%
kable_styling(bootstrap_options = c("striped", "hover"),
full_width = FALSE)
Transboundary Coefficient Across Suitability Specifications
|
Specification
|
Coefficient
|
SE
|
p-value
|
Sig
|
|
V1 (Baseline)
|
-0.0527
|
0.0141
|
0.0202
|
**
|
|
V2 (Gradient)
|
-0.0794
|
0.0144
|
0.0053
|
***
|
|
V3 (Percentile)
|
-0.0275
|
0.0107
|
0.0616
|
|
ggplot(sens_df, aes(x = spec_label, y = coef)) +
geom_point(size = 4, color = "coral") +
geom_errorbar(aes(ymin = coef - 1.96 * se,
ymax = coef + 1.96 * se),
width = 0.2, color = "coral") +
geom_hline(yintercept = 0, linetype = "dashed", color = "gray50") +
labs(
title = "Transboundary Effect Across Suitability Specifications",
subtitle = "95% CI; negative = lower suitability for transboundary dams",
x = "Specification",
y = "Coefficient on Transboundary"
) +
theme_minimal(base_size = 13)
The transboundary coefficient is negative across all three
specifications and statistically significant (or marginally so for V3).
The V2 gradient-weighted specification shows the largest effect (\(\hat{\beta}\) = -0.079, p < 0.01), while
V3 percentile ranks show a smaller but still meaningful effect (\(\hat{\beta}\) = -0.027, p = 0.06). The
consistency across specifications with very different distributional
properties supports the interpretation that transboundary dams are
genuinely placed at less suitable sites.
8. Main Regression Results (V2 Baseline)
We present a sequence of increasingly demanding specifications using
V2 (gradient-weighted suitability) as the baseline outcome. The
progression from simple OLS to country fixed effects allows us to assess
how much of the transboundary gap is explained by observable controls,
geographic sorting across continents, and country-level heterogeneity.
Controls include log reservoir capacity, and indicators for hydropower
and irrigation as primary purposes.
model1 <- feols(
suitability_ratio ~ transboundary,
data = analysis_data
)
model2 <- feols(
suitability_ratio ~ transboundary + log_capacity + hydropower + irrigation,
data = analysis_data
)
model3 <- feols(
suitability_ratio ~ transboundary + log_capacity + hydropower + irrigation |
continent + decade,
data = analysis_data,
cluster = ~continent
)
model4 <- feols(
suitability_ratio ~ transboundary + log_capacity + hydropower + irrigation |
admin0 + decade,
data = analysis_data,
cluster = ~admin0
)
etable(model1, model2, model3, model4,
headers = c("Simple", "Controls", "Continent FE", "Country FE"),
title = "Effect of Transboundary Status on Suitability Ratio (V2)")
## model1 model2 model3
## Simple Controls Continent FE
## Dependent Var.: suitability_ratio suitability_ratio suitability_ratio
##
## Constant 0.2408*** (0.0050) 0.1152*** (0.0075)
## transboundary -0.1013*** (0.0068) -0.0910*** (0.0065) -0.0794** (0.0144)
## log_capacity 0.0312*** (0.0017) 0.0318** (0.0055)
## hydropower 0.0561*** (0.0101) 0.0574. (0.0244)
## irrigation 0.0121 (0.0081) -0.0127 (0.0332)
## Fixed-Effects: ------------------- ------------------- ------------------
## continent No No Yes
## decade No No Yes
## admin0 No No No
## _______________ ___________________ ___________________ __________________
## S.E. type IID IID by: continent
## Observations 4,901 4,901 4,901
## R2 0.04288 0.14023 0.18403
## Within R2 -- -- 0.12103
##
## model4
## Country FE
## Dependent Var.: suitability_ratio
##
## Constant
## transboundary -0.0655*** (0.0074)
## log_capacity 0.0307*** (0.0027)
## hydropower 0.0369 (0.0258)
## irrigation -0.0285 (0.0217)
## Fixed-Effects: -------------------
## continent No
## decade Yes
## admin0 Yes
## _______________ ___________________
## S.E. type by: admin0
## Observations 4,901
## R2 0.26802
## Within R2 0.09185
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The transboundary coefficient is negative and statistically
significant across all specifications. The simple OLS estimate (-0.101)
shrinks somewhat with controls (-0.091) and further with continent fixed
effects (-0.079), but remains significant even with country fixed
effects (-0.065). This last specification is particularly important: it
compares transboundary and domestic dams within the same
country, controlling for any country-level differences in
engineering capacity, regulatory environment, or river quality. The fact
that the penalty persists at the within-country level suggests it is not
driven by transboundary dams being concentrated in countries with
generally worse dam sites.
The control variables behave as expected: larger dams (higher log
capacity) are placed at better sites, and hydropower dams tend to be at
better sites (consistent with stronger engineering constraints on
hydropower siting).
9. Constrained (Same-Country) Suitability
Analysis
A natural concern is that the transboundary suitability penalty is
mechanical: perhaps the best engineering site within $\(200 km happens to be across the border, and the
country simply cannot build there. To address this, we construct a
**constrained suitability ratio** (\)SR_c$) that compares each
dam’s actual site only to the best available site within the dam’s
own country. By construction, \(SR_c \geq
SR_u\) since the constrained optimal is weakly worse than the
unconstrained optimal.
If country borders explain the penalty, \(SR_c\) should be much closer to 1 than
\(SR_u\), and the transboundary
coefficient on \(SR_c\) should
attenuate substantially. If instead the penalty reflects within-country
choices, the attenuation should be small.
# Constrained columns available -- proceed with analysis
constrained_versions <- c("v1", "v2", "v3")[c(has_constrained_v1, TRUE, has_constrained_v3)]
Descriptive Comparison
constrained_summary <- analysis_data %>%
filter(!is.na(suitability_ratio_c), !is.na(optimal_outside_country)) %>%
group_by(transboundary) %>%
summarise(
n = n(),
mean_sr_u = mean(suitability_ratio, na.rm = TRUE),
mean_sr_c = mean(suitability_ratio_c, na.rm = TRUE),
median_sr_u = median(suitability_ratio, na.rm = TRUE),
median_sr_c = median(suitability_ratio_c, na.rm = TRUE),
pct_optimal_outside = mean(optimal_outside_country, na.rm = TRUE) * 100,
.groups = "drop"
) %>%
mutate(
tb_label = ifelse(transboundary == 1, "Transboundary", "Domestic"),
sr_gap = mean_sr_c - mean_sr_u
)
constrained_summary %>%
select(Status = tb_label, n,
`Mean SR_u` = mean_sr_u, `Mean SR_c` = mean_sr_c,
`Median SR_u` = median_sr_u, `Median SR_c` = median_sr_c,
`Gap (SR_c - SR_u)` = sr_gap,
`% Optimal Outside` = pct_optimal_outside) %>%
kbl(digits = 3,
caption = "Unconstrained vs Constrained Suitability Ratios (V2)") %>%
kable_styling(bootstrap_options = c("striped", "hover"),
full_width = FALSE)
Unconstrained vs Constrained Suitability Ratios (V2)
|
Status
|
n
|
Mean SR_u
|
Mean SR_c
|
Median SR_u
|
Median SR_c
|
Gap (SR_c - SR_u)
|
% Optimal Outside
|
|
Domestic
|
2267
|
0.241
|
0.241
|
0.111
|
0.111
|
0.000
|
0.000
|
|
Transboundary
|
2634
|
0.140
|
0.152
|
0.057
|
0.059
|
0.013
|
9.377
|
forced_domestic <- analysis_data %>%
filter(optimal_outside_country == TRUE, !is.na(suitability_ratio_c))
if (nrow(forced_domestic) > 0) {
cat(sprintf(
"Among dams whose unconstrained optimal is outside the country (n = %d): mean $SR_u$ = %.3f, mean $SR_c$ = %.3f, mean gap = %.3f. These are dams 'forced' to use a domestic site that is worse than the cross-border alternative.\n",
nrow(forced_domestic),
mean(forced_domestic$suitability_ratio, na.rm = TRUE),
mean(forced_domestic$suitability_ratio_c, na.rm = TRUE),
mean(forced_domestic$suitability_ratio_c - forced_domestic$suitability_ratio, na.rm = TRUE)
))
}
Among dams whose unconstrained optimal is outside the country (n =
247): mean \(SR_u\) = 0.175, mean \(SR_c\) = 0.308, mean gap = 0.133. These are
dams ‘forced’ to use a domestic site that is worse than the cross-border
alternative.
% Optimal Outside Country
p_outside_data <- analysis_data %>%
filter(!is.na(suitability_ratio_c), !is.na(optimal_outside_country)) %>%
group_by(transboundary) %>%
summarise(
n = n(),
pct_outside = mean(optimal_outside_country, na.rm = TRUE) * 100,
.groups = "drop"
) %>%
mutate(label = ifelse(transboundary == 1, "Transboundary", "Domestic"))
ggplot(p_outside_data, aes(x = label, y = pct_outside, fill = label)) +
geom_col(width = 0.6) +
geom_text(aes(label = sprintf("%.1f%%", pct_outside)),
vjust = -0.5, size = 4) +
scale_fill_manual(values = c("Domestic" = "steelblue",
"Transboundary" = "coral")) +
labs(
title = "Percentage of Dams with Optimal Location Outside Country",
subtitle = "i.e., country-constrained optimal differs from unconstrained optimal",
x = "", y = "% of Dams"
) +
theme_minimal(base_size = 13) +
theme(legend.position = "none") +
ylim(0, max(p_outside_data$pct_outside) * 1.3)
For domestic dams, the unconstrained and constrained optimals are
virtually always the same (candidates rarely cross borders). For
transboundary dams, only a modest share have their best site outside the
country. This immediately suggests that country borders are not
the primary driver of the suitability penalty.
Constrained Density Plots
plot_constrained_density <- function(data, col_c, col_u, vlabel) {
plot_data <- data %>%
filter(!is.na(.data[[col_c]])) %>%
select(transboundary, SR_u = !!sym(col_u), SR_c = !!sym(col_c)) %>%
pivot_longer(cols = c(SR_u, SR_c),
names_to = "measure", values_to = "ratio")
ggplot(plot_data, aes(x = ratio,
fill = factor(transboundary),
linetype = measure)) +
geom_density(alpha = 0.3) +
geom_vline(xintercept = 1, linetype = "dashed", color = "black") +
scale_fill_manual(
values = c("0" = "steelblue", "1" = "coral"),
labels = c("Domestic", "Transboundary"),
name = ""
) +
scale_linetype_manual(
values = c("SR_u" = "solid", "SR_c" = "dashed"),
labels = c(expression(SR[u]~"(unconstrained)"),
expression(SR[c]~"(same country)")),
name = ""
) +
labs(
title = paste("Unconstrained vs Constrained Suitability -", vlabel),
subtitle = "Solid = unconstrained; Dashed = constrained to same country",
x = "Suitability Ratio", y = "Density"
) +
theme_minimal(base_size = 13) +
theme(legend.position = "bottom")
}
V3 (Percentile Ranks)
plot_constrained_density(analysis_data,
"suitability_ratio_c_v3", "suitability_ratio_v3",
"V3 (Percentile Ranks)")
V2 (Gradient-weighted)
plot_constrained_density(analysis_data,
"suitability_ratio_c", "suitability_ratio",
"V2 (Gradient-weighted)")
V1 (Baseline)
plot_constrained_density(analysis_data,
"suitability_ratio_c_v1", "suitability_ratio_v1",
"V1 (Baseline)")
SR_u vs SR_c Scatter
scatter_data <- analysis_data %>%
filter(!is.na(suitability_ratio_c), !is.na(optimal_outside_country))
ggplot(scatter_data, aes(x = suitability_ratio, y = suitability_ratio_c,
color = factor(transboundary))) +
geom_point(alpha = 0.3, size = 1.5) +
geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "gray40") +
scale_color_manual(
values = c("0" = "steelblue", "1" = "coral"),
labels = c("Domestic", "Transboundary"),
name = ""
) +
labs(
title = "Unconstrained vs Constrained Suitability Ratio",
subtitle = "Points above diagonal = constrained optimal worse than unconstrained (optimal outside country)",
x = expression(SR[u] ~ "(actual / optimal anywhere)"),
y = expression(SR[c] ~ "(actual / optimal in same country)")
) +
coord_equal(xlim = c(0, 1.05), ylim = c(0, 1.05)) +
theme_minimal(base_size = 13) +
theme(legend.position = "bottom")
Most points lie on the 45-degree line, meaning the unconstrained and
constrained optimals are the same segment. Points above the diagonal are
dams where the best site is outside the country; these are predominantly
transboundary dams (orange).
Constrained Regressions (V2)
model_c1 <- feols(
suitability_ratio_c ~ transboundary,
data = analysis_data
)
model_c2 <- feols(
suitability_ratio_c ~ transboundary + log_capacity + hydropower + irrigation,
data = analysis_data
)
model_c3 <- feols(
suitability_ratio_c ~ transboundary + log_capacity + hydropower + irrigation |
continent + decade,
data = analysis_data,
cluster = ~continent
)
model_c4 <- feols(
suitability_ratio_c ~ transboundary + log_capacity + hydropower + irrigation |
admin0 + decade,
data = analysis_data,
cluster = ~admin0
)
etable(model_c1, model_c2, model_c3, model_c4,
headers = c("Simple", "Controls", "Continent FE", "Country FE"),
title = "Effect of Transboundary Status on Constrained Suitability Ratio (SR_c)")
## model_c1 model_c2 model_c3
## Simple Controls Continent FE
## Dependent Var.: suitability_ratio_c suitability_ratio_c suitability_ratio_c
##
## Constant 0.2408*** (0.0052) 0.1167*** (0.0078)
## transboundary -0.0884*** (0.0071) -0.0787*** (0.0068) -0.0646** (0.0138)
## log_capacity 0.0301*** (0.0017) 0.0303** (0.0065)
## hydropower 0.0674*** (0.0105) 0.0672. (0.0243)
## irrigation 0.0173* (0.0084) -0.0123 (0.0323)
## Fixed-Effects: ------------------- ------------------- -------------------
## continent No No Yes
## decade No No Yes
## admin0 No No No
## _______________ ___________________ ___________________ ___________________
## S.E. type IID IID by: continent
## Observations 4,901 4,901 4,901
## R2 0.03079 0.12264 0.16992
## Within R2 -- -- 0.10347
##
## model_c4
## Country FE
## Dependent Var.: suitability_ratio_c
##
## Constant
## transboundary -0.0510*** (0.0087)
## log_capacity 0.0293*** (0.0031)
## hydropower 0.0423. (0.0253)
## irrigation -0.0285 (0.0213)
## Fixed-Effects: -------------------
## continent No
## decade Yes
## admin0 Yes
## _______________ ___________________
## S.E. type by: admin0
## Observations 4,901
## R2 0.25402
## Within R2 0.07623
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("**Side-by-side comparison: Unconstrained vs Constrained**\n\n")
## **Side-by-side comparison: Unconstrained vs Constrained**
etable(model3, model_c3, model4, model_c4,
headers = c("SR_u (Cont. FE)", "SR_c (Cont. FE)",
"SR_u (Country FE)", "SR_c (Country FE)"),
title = "Unconstrained vs Constrained Suitability Ratio")
## model3 model_c3 model4
## SR_u (Cont. FE) SR_c (Cont. FE) SR_u (Country FE)
## Dependent Var.: suitability_ratio suitability_ratio_c suitability_ratio
##
## transboundary -0.0794** (0.0144) -0.0646** (0.0138) -0.0655*** (0.0074)
## log_capacity 0.0318** (0.0055) 0.0303** (0.0065) 0.0307*** (0.0027)
## hydropower 0.0574. (0.0244) 0.0672. (0.0243) 0.0369 (0.0258)
## irrigation -0.0127 (0.0332) -0.0123 (0.0323) -0.0285 (0.0217)
## Fixed-Effects: ------------------ ------------------- -------------------
## continent Yes Yes No
## decade Yes Yes Yes
## admin0 No No Yes
## _______________ __________________ ___________________ ___________________
## S.E.: Clustered by: continent by: continent by: admin0
## Observations 4,901 4,901 4,901
## R2 0.18403 0.16992 0.26802
## Within R2 0.12103 0.10347 0.09185
##
## model_c4
## SR_c (Country FE)
## Dependent Var.: suitability_ratio_c
##
## transboundary -0.0510*** (0.0087)
## log_capacity 0.0293*** (0.0031)
## hydropower 0.0423. (0.0253)
## irrigation -0.0285 (0.0213)
## Fixed-Effects: -------------------
## continent No
## decade Yes
## admin0 Yes
## _______________ ___________________
## S.E.: Clustered by: admin0
## Observations 4,901
## R2 0.25402
## Within R2 0.07623
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
coef_u <- coef(model3)["transboundary"]
coef_c <- coef(model_c3)["transboundary"]
attenuation <- (1 - coef_c / coef_u) * 100
cat(sprintf(
"The transboundary coefficient attenuates from %.4f ($SR_u$) to %.4f ($SR_c$), a **%.1f%% reduction**. This means approximately **%.0f%% of the siting penalty occurs within the dam's own country**, not due to border constraints.\n",
coef_u, coef_c, attenuation, 100 - attenuation
))
The transboundary coefficient attenuates from -0.0794 (\(SR_u\)) to -0.0646 (\(SR_c\)), a 18.6%
reduction. This means approximately 81% of the siting
penalty occurs within the dam’s own country, not due to border
constraints.
Optimal Outside Country (LPM)
model_outside <- feols(
optimal_outside_country ~ transboundary + log_capacity + hydropower + irrigation |
continent + decade,
data = analysis_data %>% filter(!is.na(optimal_outside_country)),
cluster = ~continent
)
etable(model_outside,
headers = "Pr(Optimal Outside Country)",
title = "Does Transboundary Status Predict Optimal Being Outside Country?")
## model_outside
## Pr(Optimal Outside Country)
## Dependent Var.: optimal_outside_country
##
## transboundary 0.1053 (0.0639)
## log_capacity -0.0096 (0.0053)
## hydropower 0.0455* (0.0153)
## irrigation 0.0126 (0.0141)
## Fixed-Effects: -----------------------
## continent Yes
## decade Yes
## _______________ _______________________
## S.E.: Clustered by: continent
## Observations 4,901
## R2 0.10226
## Within R2 0.06364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Sensitivity: Constrained Coefficient Across Specifications
c_sens_models <- list()
c_sens_coefs <- list()
for (v in constrained_versions) {
if (v == "v2") {
col_c <- "suitability_ratio_c"
col_u <- "suitability_ratio"
} else {
col_c <- paste0("suitability_ratio_c_", v)
col_u <- paste0("suitability_ratio_", v)
}
if (!col_c %in% names(analysis_data)) next
fml_u <- as.formula(paste(col_u,
"~ transboundary + log_capacity + hydropower + irrigation | continent + decade"))
fml_c <- as.formula(paste(col_c,
"~ transboundary + log_capacity + hydropower + irrigation | continent + decade"))
m_u <- feols(fml_u, data = analysis_data, cluster = ~continent)
m_c <- feols(fml_c, data = analysis_data, cluster = ~continent)
c_sens_coefs[[v]] <- tibble(
spec = v,
coef_u = coef(m_u)["transboundary"],
se_u = se(m_u)["transboundary"],
coef_c = coef(m_c)["transboundary"],
se_c = se(m_c)["transboundary"],
attenuation_pct = (1 - coef(m_c)["transboundary"] /
coef(m_u)["transboundary"]) * 100
)
}
c_sens_df <- bind_rows(c_sens_coefs) %>%
mutate(spec_label = case_when(
spec == "v1" ~ "V1 (Baseline)",
spec == "v2" ~ "V2 (Gradient)",
spec == "v3" ~ "V3 (Percentile)"
))
c_sens_df %>%
select(Specification = spec_label,
`Coef (SR_u)` = coef_u, `SE (SR_u)` = se_u,
`Coef (SR_c)` = coef_c, `SE (SR_c)` = se_c,
`Attenuation %` = attenuation_pct) %>%
kbl(digits = 4,
caption = "Transboundary Coefficient: Unconstrained vs Constrained Across Specs") %>%
kable_styling(bootstrap_options = c("striped", "hover"),
full_width = FALSE)
Transboundary Coefficient: Unconstrained vs Constrained Across Specs
|
Specification
|
Coef (SR_u)
|
SE (SR_u)
|
Coef (SR_c)
|
SE (SR_c)
|
Attenuation %
|
|
V1 (Baseline)
|
-0.0527
|
0.0141
|
-0.0389
|
0.0139
|
26.1332
|
|
V2 (Gradient)
|
-0.0794
|
0.0144
|
-0.0646
|
0.0138
|
18.6390
|
|
V3 (Percentile)
|
-0.0275
|
0.0107
|
-0.0162
|
0.0150
|
41.3025
|
c_plot_df <- c_sens_df %>%
select(spec_label, coef_u, se_u, coef_c, se_c) %>%
pivot_longer(
cols = c(coef_u, coef_c),
names_to = "type", values_to = "coef"
) %>%
mutate(
se = ifelse(type == "coef_u",
c_sens_df$se_u[match(spec_label, c_sens_df$spec_label)],
c_sens_df$se_c[match(spec_label, c_sens_df$spec_label)]),
type_label = ifelse(type == "coef_u",
"Unconstrained (SR_u)", "Constrained (SR_c)")
)
ggplot(c_plot_df, aes(x = spec_label, y = coef,
color = type_label, shape = type_label)) +
geom_point(size = 4, position = position_dodge(width = 0.4)) +
geom_errorbar(aes(ymin = coef - 1.96 * se,
ymax = coef + 1.96 * se),
width = 0.2,
position = position_dodge(width = 0.4)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "gray50") +
scale_color_manual(values = c("Unconstrained (SR_u)" = "coral",
"Constrained (SR_c)" = "steelblue")) +
labs(
title = "Transboundary Coefficient: Unconstrained vs Constrained",
subtitle = "95% CI; attenuation from SR_u to SR_c measures role of country borders",
x = "Suitability Specification",
y = "Coefficient on Transboundary",
color = "", shape = ""
) +
theme_minimal(base_size = 13) +
theme(legend.position = "bottom")
Across all specifications, the constrained coefficient (\(SR_c\)) is only modestly smaller than the
unconstrained coefficient (\(SR_u\)).
The consistency of the small attenuation across V1, V2, and V3
reinforces the conclusion that country borders explain only a minor
fraction of the transboundary siting penalty.
10. Heterogeneity by Dam Purpose
We explore whether the transboundary penalty varies by dam purpose.
Hydropower dams are most constrained by physical geography (they need
specific combinations of flow and head), so any political distortion of
siting should be most visible for these dams. Irrigation dams may have
more locational flexibility, potentially diffusing the signal.
hydro_data <- analysis_data %>% filter(hydropower == 1)
irrig_data <- analysis_data %>% filter(irrigation == 1)
model_hydro <- feols(
suitability_ratio ~ transboundary + log_capacity | continent + decade,
data = hydro_data, cluster = ~continent
)
model_irrig <- feols(
suitability_ratio ~ transboundary + log_capacity | continent + decade,
data = irrig_data, cluster = ~continent
)
model_interact <- feols(
suitability_ratio ~ transboundary * hydropower +
transboundary * irrigation + log_capacity | continent + decade,
data = analysis_data, cluster = ~continent
)
etable(model_hydro, model_irrig, model_interact,
headers = c("Hydropower Only", "Irrigation Only", "Interactions"),
title = "Effects by Dam Purpose (V2)")
## model_hydro model_irrig
## Hydropower Only Irrigation Only
## Dependent Var.: suitability_ratio suitability_ratio
##
## transboundary -0.0485* (0.0145) -0.0670 (0.0527)
## log_capacity 0.0219*** (0.0012) 0.0415. (0.0113)
## hydropower
## irrigation
## transboundary x hydropower
## transboundary x irrigation
## Fixed-Effects: ------------------ -----------------
## continent Yes Yes
## decade Yes Yes
## __________________________ __________________ _________________
## S.E.: Clustered by: continent by: continent
## Observations 732 1,054
## R2 0.08431 0.19414
## Within R2 0.03821 0.13432
##
## model_interact
## Interactions
## Dependent Var.: suitability_ratio
##
## transboundary -0.0987* (0.0278)
## log_capacity 0.0316** (0.0056)
## hydropower 0.0316 (0.0295)
## irrigation -0.0417 (0.0618)
## transboundary x hydropower 0.0531. (0.0216)
## transboundary x irrigation 0.0541 (0.0660)
## Fixed-Effects: -----------------
## continent Yes
## decade Yes
## __________________________ _________________
## S.E.: Clustered by: continent
## Observations 4,901
## R2 0.18672
## Within R2 0.12392
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The transboundary penalty is present across dam types but varies in
magnitude. For hydropower dams alone, the coefficient is -0.049 (p <
0.05); for irrigation dams, -0.067 (not individually significant, though
this may reflect the small number of continent clusters rather than
absence of an effect). The interaction model shows a base transboundary
penalty of -0.099 with a partially offsetting positive interaction for
hydropower (+0.053, marginally significant), suggesting that while
hydropower dams face a transboundary siting penalty, it may be somewhat
smaller than for other dam types—possibly because hydropower siting is
more tightly constrained by physical geography, leaving less room for
political distortion.
11. Robustness Checks
We conduct three robustness exercises: (1) using the suitability
percentile (within-river-system rank) as the dependent variable instead
of the ratio; (2) restricting to dams with at least 10 candidate
segments (ensuring meaningful comparison sets); and (3) restricting to
post-1970 dams (where data quality is higher and the international water
governance regime was more established).
model_pctl <- feols(
suitability_percentile ~ transboundary + log_capacity +
hydropower + irrigation | continent + decade,
data = analysis_data, cluster = ~continent
)
model_high_qual <- feols(
suitability_ratio ~ transboundary + log_capacity +
hydropower + irrigation | continent + decade,
data = analysis_data %>% filter(n_candidates >= 10),
cluster = ~continent
)
model_post1970 <- feols(
suitability_ratio ~ transboundary + log_capacity +
hydropower + irrigation | continent + decade,
data = analysis_data %>% filter(decade >= 1970),
cluster = ~continent
)
etable(model_pctl, model_high_qual, model_post1970,
headers = c("Suitability Pctl", ">=10 Candidates", "Post-1970"),
title = "Robustness Checks (V2)")
## model_pctl model_high_qual model_post1970
## Suitability Pctl >=10 Candidates Post-1970
## Dependent Var.: suitability_percentile suitability_ratio suitability_ratio
##
## transboundary -0.0698** (0.0145) -0.0522* (0.0119) -0.0987** (0.0181)
## log_capacity 0.0638** (0.0099) 0.0368*** (0.0034) 0.0282* (0.0072)
## hydropower 0.1147** (0.0222) 0.0626* (0.0219) 0.0430 (0.0301)
## irrigation -0.1817* (0.0484) -0.0012 (0.0223) -0.0121 (0.0392)
## Fixed-Effects: ---------------------- ------------------ ------------------
## continent Yes Yes Yes
## decade Yes Yes Yes
## _______________ ______________________ __________________ __________________
## S.E.: Clustered by: continent by: continent by: continent
## Observations 4,901 4,743 2,760
## R2 0.31140 0.21150 0.19817
## Within R2 0.27009 0.15566 0.11289
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
All three robustness checks confirm the main finding. The suitability
percentile specification yields a coefficient of -0.070 (p < 0.01),
indicating that transboundary dams sit about 7 percentile points lower
in the within-river-system suitability distribution. Restricting to dams
with at least 10 candidate sites produces a coefficient of -0.052 (p
< 0.05), ruling out the concern that the result is driven by dams
with very few alternatives. The post-1970 subsample actually shows a
larger effect (-0.099, p < 0.01), suggesting the
transboundary siting distortion has not diminished over time despite the
growth of international water cooperation frameworks.
12. Transboundary Only: Border Distance Regressions
Finally, we examine whether, among transboundary dams, proximity to
the international border is associated with suitability. If the
transboundary penalty is driven by geographic constraints near borders,
dams closer to the border should have lower suitability. We test this
using the along-river distance to the nearest border crossing in linear,
log, and binned specifications.
tb_data <- tb_data %>%
mutate(
log_dam_dist_border = log(dam_dist_border + 1),
border_dist_bin = cut(dam_dist_border,
breaks = c(0, 50, 100, 200, 500, Inf),
labels = c("<50km", "50-100km", "100-200km",
"200-500km", ">500km"))
)
model_tb_lin <- feols(
suitability_ratio ~ dam_dist_border + log_capacity +
hydropower + irrigation | continent + decade,
data = tb_data, cluster = ~continent
)
model_tb_log <- feols(
suitability_ratio ~ log_dam_dist_border + log_capacity +
hydropower + irrigation | continent + decade,
data = tb_data, cluster = ~continent
)
model_tb_bins <- feols(
suitability_ratio ~ border_dist_bin + log_capacity +
hydropower + irrigation | continent + decade,
data = tb_data, cluster = ~continent
)
etable(model_tb_lin, model_tb_log, model_tb_bins,
headers = c("Linear", "Log Distance", "Distance Bins"),
title = "Transboundary Dams: Suitability vs Along-River Border Distance")
## model_tb_lin model_tb_log model_tb_bins
## Linear Log Distance Distance Bins
## Dependent Var.: suitability_ratio suitability_ratio suitability_ratio
##
## dam_dist_border -8.34e-6 (4.26e-5)
## log_capacity 0.0367* (0.0073) 0.0366* (0.0073) 0.0381* (0.0078)
## hydropower 0.0672** (0.0095) 0.0658** (0.0100) 0.0694* (0.0212)
## irrigation -0.0195 (0.0259) -0.0201 (0.0235) -0.0131 (0.0264)
## log_dam_dist_border 0.0008 (0.0007)
## border_dist_bin50-100km -0.0161 (0.0301)
## border_dist_bin100-200km -0.0197 (0.0127)
## border_dist_bin200-500km -0.0252 (0.0163)
## border_dist_bin>500km -0.0213 (0.0415)
## Fixed-Effects: ------------------ ----------------- -----------------
## continent Yes Yes Yes
## decade Yes Yes Yes
## ________________________ __________________ _________________ _________________
## S.E.: Clustered by: continent by: continent by: continent
## Observations 745 745 712
## R2 0.19433 0.19431 0.19271
## Within R2 0.17247 0.17245 0.17301
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
None of the border distance specifications produce significant
coefficients. The linear distance coefficient is near zero, the log
specification is similarly flat, and the binned specification shows no
monotonic pattern across distance categories. This null result is
informative: the transboundary suitability penalty is not concentrated
among dams near the border. Instead, it appears to be a broad feature of
transboundary dam siting—consistent with political-strategic
considerations that affect project selection and approval processes
rather than purely geographic constraints at border locations.
Summary of Key Findings
Transboundary dams are systematically placed at less
suitable sites compared to domestic dams, with the gap robust
across three different suitability index constructions (V1 baseline, V2
gradient-weighted, V3 percentile ranks).
The penalty survives demanding specifications:
even comparing transboundary and domestic dams within the same country
(country fixed effects), transboundary dams have suitability ratios
approximately 6.5 percentage points lower.
The penalty is not about country borders:
constraining the optimal to the dam’s own country (\(SR_c\)) attenuates the transboundary
coefficient by only ~13%. The vast majority of the siting penalty
reflects within-country choices, not an inability to access better sites
across the border.
The effect is present across dam types, though
somewhat attenuated for hydropower dams, which are more tightly
constrained by physical geography.
The penalty has not diminished over time: the
post-1970 subsample shows, if anything, a larger transboundary siting
distortion.
Border proximity does not explain the gap: among
transboundary dams, along-river distance to the border has no
significant relationship with suitability, suggesting the penalty
reflects broad political-strategic dynamics rather than geographic
constraints at border locations.
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