df_a <- data.frame( Region = rep( c(“East Asia”, “Eastern
EuropeCentral Asia”, “Latin AmericaCaribbean”, “Middle EastNorth
Africa”, “Northern America”, “South Asia”, “Southeast AsiaOceania”,
“Sub-Saharan Africa”, “Western Europe”), each = 2 ), Type =
rep(c(“Low-carbon electricity”, “Drip”), 9), Feasibility = c( 31, 24, #
East Asia 21, 24, # Eastern Europe and Central Asia 30, 18, # Latin
America and Caribbean 49, 40, # Middle East and North Africa 22, 16, #
Northern America 20.5, 9.5,# South Asia 33, 4, # Southeast Asia and
Oceania 31, 18, # Sub-Saharan Africa 31, 29.5 # Western Europe ) ) world
<- ne_countries(scale = “medium”, returnclass = “sf”) message(“✅
世界地图底图加载成功！”) df_b <- data.frame( Region = c(“Global”,
“Northern America”, “Western Europe”, “Eastern EuropeCentral Asia”,
“East Asia”, “Latin AmericaCaribbean”, “Middle EastNorth Africa”, “South
Asia”, “Southeast AsiaOceania”, “Sub-Saharan Africa”), Lat = c(0, 37,
48, 50, 35, -10, 25, 20, 0, 10),
Lon = c(0, -95, 10, 25, 105, -60, 45, 75, 120, 20), Low_carbon = c(55,
64, 63, 15, 52, 51, 66, 53, 42, 45), Drip = c(45, 36, 37, 85, 48, 49,
34, 47, 58, 55), Size = c(100, 80, 90, 70, 85, 75, 95, 65, 80, 70) )
plot_a <- ggplot(df_a, aes(x = Region, y = Feasibility, fill = Type))
+ geom_col(position = position_dodge(width = 0.8), width = 0.7, color =
“black”, linewidth = 0.3) + scale_fill_manual(values = c(“Low-carbon
electricity” = “#fccf00”, “Drip” = “#006660”)) + labs(x = NULL, y =
“Feasibility (%)”, fill = NULL) + theme_minimal() + theme(
panel.grid.major.x = element_blank(), panel.grid.minor =
element_blank(), axis.text.x = element_text(angle = 45, hjust = 1, size
= 9, color = “black”), axis.text.y = element_text(size = 10, color =
“black”), axis.title.y = element_text(size = 12, color = “black”),
legend.position = “top”, legend.text = element_text(size = 10, color =
“black”), plot.margin = margin(10, 10, 10, 10) ) + ylim(0, 55) plot_b
<- ggplot() + geom_sf(data = world, fill = “#f5f5f5”, color =
“gray30”, linewidth = 0.2) + geom_scatterpie( data = df_b, aes(x = Lon,
y = Lat, r = log10(Size + 1) * 1.2), cols = c(“Low_carbon”, “Drip”),
color = “black”, linewidth = 0.3, alpha = 0.9 ) +
scale_fill_manual(values = c(“Low_carbon” = “#fccf00”, “Drip” =
“#006660”)) + coord_sf(xlim = c(-180, 180), ylim = c(-60, 80), expand =
FALSE) + labs(x = NULL, y = NULL, fill = “Contribution to CO₂ emissions
reduction (%)”) + theme_void() + theme( legend.position = “bottom”,
legend.text = element_text(size = 9, color = “black”), legend.title =
element_text(size = 10, color = “black”), plot.margin = margin(5, 5, 5,
5) ) + annotate(“text”, x = -170, y = 20, label = “Global(55%)”, hjust =
0, size = 3.5) + annotate(“text”, x = -100, y = 45, label = “Northern
America(64%)”, hjust = 0, size = 3.5) + annotate(“text”, x = 0, y = 55,
label = “Western Europe(63%)”, hjust = 0, size = 3.5) + annotate(“text”,
x = 30, y = 55, label = “Eastern Europe andAsia (15%)”, hjust = 0, size
= 3.5) + annotate(“text”, x = 110, y = 45, label = “East Asia(52%)”,
hjust = 0, size = 3.5) + annotate(“text”, x = -70, y = -20, label =
“Latin America and(51%)”, hjust = 0, size = 3.5) + annotate(“text”, x =
30, y = 30, label = “Middle East andAfrica (66%)”, hjust = 0, size =
3.5) + annotate(“text”, x = 80, y = 30, label = “South Asia(53%)”, hjust
= 0, size = 3.5) + annotate(“text”, x = 125, y = 0, label = “Southeast
Asia and(42%)”, hjust = 0, size = 3.5) + annotate(“text”, x = 25, y = 0,
label = “Sub-Saharan Africa(45%)”, hjust = 0, size = 3.5) final_plot
<- plot_a / plot_b + plot_annotation( tag_levels = “a”, theme =
theme(plot.tag = element_text(size = 14, face = “bold”)) )
print(final_plot) ggsave( “replicated_figure_final.png”, final_plot,
width = 14, height = 10, dpi = 300, bg = “white” )
regions <- c( “East Asia”, “Western Europe”, “Middle East and North Africa”, “North America”, “Latin America and Caribbean”, “South Asia”, “Southeast Asia and Oceania”, “Eastern Europe and Central Asia”, “Sub-Saharan Africa”, “Global” ) # 图a：能源投入强度 (GJ/ha) energy_data <- data.frame( Region = factor(regions, levels = regions), Fuel = c(20.5, 15.5, 1.8, 3.8, 1.5, 0.5, 0.8, 3.2, 0.2, 5.2), Machinery = c(7.2, 6.8, 1.2, 2.2, 1.2, 0.3, 0.5, 1.8, 0.3, 2.3), Fertilizers_production_transport = c(5.3, 4.2, 3.8, 2.8, 1.0, 2.5, 2.8, 2.5, 0.5, 3.0), Irrigation = c(3.5, 2.5, 13.0, 5.7, 6.5, 5.5, 4.2, 0.8, 6.5, 5.0) )
emission_data <- data.frame( Region = factor(regions, levels = regions), Fuel = c(1450, 1050, 120, 280, 100, 80, 60, 220, 30, 380), Machinery = c(720, 600, 80, 220, 120, 70, 80, 180, 40, 220), Fertilizers_production_transport = c(600, 650, 400, 300, 150, 180, 250, 250, 50, 300), Fertilizers_use = c(180, 250, 200, 250, 550, 150, 60, 80, 0, 200), Irrigation = c(350, 300, 1800, 650, 500, 500, 450, 180, 700, 600) ) # 图a：宽转长 energy_long <- energy_data %>% pivot_longer(cols = -Region, names_to = “Category”, values_to = “Value”) %>% mutate( Category = factor( Category, levels = c(“Fuel”, “Machinery”, “Fertilizers_production_transport”, “Irrigation”), labels = c(“Fuel”, “Machinery”, “Fertilizers production and transport”, “Irrigation”) ) ) emission_long <- emission_data %>% pivot_longer(cols = -Region, names_to = “Category”, values_to = “Value”) %>% mutate( Category = factor( Category, levels = c(“Fuel”, “Machinery”, “Fertilizers_production_transport”, “Fertilizers_use”, “Irrigation”), labels = c(“Fuel”, “Machinery”, “Fertilizers production and transport”, “Fertilizers use”, “Irrigation”) ) ) energy_irrigation_pct <- energy_data %>% mutate( Total = rowSums(select(., -Region)), Irrigation_pct = round(Irrigation / Total * 100, 0) ) %>% select(Region, Irrigation_pct, Total) emission_irrigation_pct <- emission_data %>% mutate( Total = rowSums(select(., -Region)), Irrigation_pct = round(Irrigation / Total * 100, 0) ) %>% select(Region, Irrigation_pct, Total) energy_colors <- c( “Fuel” = “#a8dba8”, “Machinery” = “#7fbfd0”, “Fertilizers production and transport” = “#3b8bc0”, “Irrigation” = “#f7941d” )
emission_colors <- c( “Fuel” = “#a8dba8”, “Machinery” = “#7fbfd0”, “Fertilizers production and transport” = “#3b8bc0”, “Fertilizers use” = “#f2c078”, “Irrigation” = “#f7941d” ) p_a <- ggplot(energy_long, aes(x = Region, y = Value, fill = Category)) + geom_col(position = “stack”, width = 0.8, color = “white”, linewidth = 0.2) + # 【关键修复】给geom_text添加inherit.aes = FALSE，切断主图层aes继承 geom_text( data = energy_irrigation_pct, aes(x = Region, y = Total, label = paste0(Irrigation_pct, “%”)), vjust = -0.3, size = 3.5, fontface = “bold”, inherit.aes = FALSE # 核心修复：不继承主图层的fill=Category映射 ) + scale_y_continuous( limits = c(0, 40), expand = c(0, 0), breaks = seq(0, 40, 5) ) + scale_fill_manual(values = energy_colors) + theme_bw(base_size = 12) + theme( axis.text.x = element_text(angle = 45, hjust = 1, size = 10), axis.text.y = element_text(size = 10), axis.title = element_text(size = 11), legend.position = “top”, legend.title = element_blank(), legend.text = element_text(size = 10), panel.grid.major.x = element_blank(), panel.grid.minor = element_blank(), plot.tag = element_text(size = 14, face = “bold”, hjust = 0) ) + labs( tag = “a”, y = “Energy input intensity (GJ/ha)”, x = NULL ) p_b <- ggplot(emission_long, aes(x = Region, y = Value, fill = Category)) + geom_col(position = “stack”, width = 0.8, color = “white”, linewidth = 0.2) + # 【关键修复】同样添加inherit.aes = FALSE geom_text( data = emission_irrigation_pct, aes(x = Region, y = Total, label = paste0(Irrigation_pct, “%”)), vjust = -0.3, size = 3.5, fontface = “bold”, inherit.aes = FALSE # 核心修复 ) + scale_y_continuous( limits = c(0, 3500), expand = c(0, 0), breaks = seq(0, 3500, 500) ) + scale_fill_manual(values = emission_colors) + theme_bw(base_size = 12) + theme( axis.text.x = element_text(angle = 45, hjust = 1, size = 10), axis.text.y = element_text(size = 10), axis.title = element_text(size = 11), legend.position = “top”, legend.title = element_blank(), legend.text = element_text(size = 10), panel.grid.major.x = element_blank(), panel.grid.minor = element_blank(), plot.tag = element_text(size = 14, face = “bold”, hjust = 0) ) + labs( tag = “b”, y = expression(CO[2]eemissionsintensity(kgCO[2]-e/ha)), x = NULL ) final_plot <- p_a / p_b + plot_layout(ncol = 1, heights = c(1, 1.2)) print(final_plot) ggsave( “reproduced_agri_energy_emission.png”, plot = final_plot, width = 16, height = 10, dpi = 300, bg = “white” )